pip install -r requirements.txt- Create a new openai api key, link: https://platform.openai.com/api-keys.
- Copy it into .env file
Set OPENAI_API_KEY="Your API KEY"
Here we use the question-answer dataset related to Ubuntu from Hugging Face. We will select 100 data from the training set for fine-tuning testing, and you can also modify it in the code.
The dataset link is here.
python create_data.pyAfter running it, you will see a train_data.jsonl file appearing in this directory, which is the data format required for fine-tuning ChatGPT.
python finetune.pyAfter running the above code, you will obtain a parameter called job id in the final output. You need to copy this parameter to your notepad because it will be used later on.
It looks like "ftjob-k66N8QIGu1ehgQmGRIbhXUuD".
Next, log in to your personal OpenAI interface, where you can monitor the progress of the fine-tuning process. The link is https://platform.openai.com/finetune. You need to wait for few minutes to see the beginning of fine-tuning, and then you can see the loss curve.
Once you see the fine-tuning process has completed, you can start using your own ChatGPT. You have two methods to use it: one is to directly use through the chat interface at https://platform.openai.com/playground; alternatively, you can also use the id of the new model for local testing.
- Chat Interface
- Local Test
python test.py
During the running process, you will receive a new_model_id. You also need to save it because we will use this variable name in the next step.
Now we can test the differences between the original ChatGPT and the newly fine-tuned model. You need to pass the previously saved new_model_id into the compare.py file.
python compare.pyWe select some data from the test set of the dataset we initially chose for training, and store the labeled answers, original ChatGPT responses, and responses from our fine-tuned model in a json file and a csv file together. After the run completes, we will obtain two files named results.json and results.csv.